Parsimonious Gene Correlation Network Analysis (PGCNA): a tool to define modular gene co-expression for refined molecular stratification in cancer.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The deviation was non-trivial in magnitude
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
REPRODUCED the PGCNA pipeline (authors' own tool, github.com/medmaca/PGCNA @ 3aed133) on a real paper input, GEO GSE12276 (204 breast-cancer arrays -> 22880 genes). The paper publishes NO per-dataset numbers (GSE12276 is 1/23 BRCA datasets merged into a 7464-case/17805-gene meta-matrix), so this is graded against pinnable method-behaviour claims, not a 1:1 GSE12276 value. Results on «our HPC» (SLURM 2208308): C1 80% gene retention = EXACT (18304/22880). C2 radical edge reduction scaling with EPG = PARTIAL (relative EPG10/EPG3 edge ratio 3.29x matches paper's implied 3.6x; absolute fold differs because paper's full-network baseline is undefined/thresholded). C3 module count rises as EPG falls (EPG3=23>EPG5=18>EPG10) = direction MATCH. C4 stable high modularity (EPG3 Q=0.678, 1000 runs, 23 balanced modules) = qualitative MATCH (no published Q). Deviations recorded: GPL570 symbols vs MyGene.info; 1000 vs 10000 FastUnfold runs; single dataset; 3 minimal py3 patches. NOT attempted: full BRCA/CRC meta-matrices (pgcna-multi.py) and downstream enrichment/stratification.
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v1 current initial assessment Score 71assessed: 2026-06-20 ⛓ de3ce51b14e5
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- Reproduced
- 2026-06-20
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-20no human curator yet
- Last updated
- 2026-08-05
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Deep full-text extraction
Model: sonnetThe paper tests whether radically reducing network edges to only the most highly correlated per gene (a parsimonious approach) before clustering can better resolve biologically meaningful co-expression modules across large, multi-dataset cancer gene expression resources than existing correlation network methods.
- ★ Retaining only the top ~3 most correlated edges per gene (EPG3) combined with FastUnfold clustering (termed PGCNA) produces gene co-expression modules with significantly better separation and enrichment of known biology than using all edges or other clustering methods. finding
- ★ PGCNA is introduced as a new computationally efficient tool for Parsimonious Gene Correlation Network Analysis. method
- ★ PGCNA outperforms iPCC, power function, and WGCNA sigmoid edge-refinement methods, as well as the WGCNA package as a whole, in biological signature enrichment and gene membership stability. finding
- ★ PGCNA network solutions are scale-free across all tested EPG thresholds and retain 100% gene connectivity. finding
- ★ PGCNA-derived modules map onto established expression-based cancer classification schemes, including PAM50 intrinsic subtypes in breast cancer and consensus molecular subtypes (CMS) in colorectal cancer. finding
- ★ Co-expression modules identified independently in BRCA and CRC show highly significant overlap in gene membership, indicating recurrent, stable, cancer-type-independent modules. finding
- Module expression values (MEVs) can be used to summarize network-level gene expression per case, enabling classification and analysis of linkage between module expression and mutational state. method
- Immune response-related modules show selective association of immune subsets, particularly mast cells, with underlying mutational patterns in breast cancer. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| gene expression profiling (multi-dataset correlation network analysis) | breast cancer (BRCA) patient expression datasets (n=26) | none | gene-gene co-expression correlation network modules | — |
| gene expression profiling (multi-dataset correlation network analysis) | colorectal cancer (CRC) patient expression datasets (n=11) | none | gene-gene co-expression correlation network modules | — |
| gene ontology and signature enrichment analysis | BRCA and CRC gene co-expression modules | none | scaled cluster enrichment score (SCES) | — |
| comparative network clustering (FastUnfold vs hierarchical vs k-means; EPG vs iPCC vs power function vs WGCNA sigmoid) | representative large BRCA and CRC expression datasets | edge reduction method/threshold (EPG3-EPG10, sigmoid μ) | module number, module size, SCES, percentage of connected genes | — |
| multi-omic integration (expression module mapping with mutation data) | TCGA BRCA and CRC cases | mutational state | association between module expression value (MEV) and mutation pattern at dataset and single-case level | — |
| immune subset association analysis via module expression | TCGA breast cancer cases (immune response-related modules) | none | association of mast cell/immune subset module expression with mutational pattern | — |
| pairwise module comparison (hypergeometric test) | BRCA vs CRC network modules | none | significance of overlap by gene membership and enriched gene signatures | — |
- ▲ EPG parsimonious matrices combined with FastUnfold clustering gave the most significant enrichment and segregation of ontology/signature terms compared to unreduced ('All') matrices and to hierarchical or k-means clustering.
- ▼ Radical edge reduction thresholds (EPG3-EPG10) reduced total network edges linearly relative to dataset size. 250-fold (EPG10) to 900-fold (EPG3)
- – No significant additional benefit was found from retaining more than 3 edges per gene (EPG3), while module number remained manageable.
- ▲ EPG3/FastUnfold retained 100% gene connectivity, compared to sigmoid function at highest μ which left substantial percentages of orphan (unconnected) genes. 100% vs 32%/61% (BRCA/CRC)
- – iPCC and power function edge-refinement methods produced minimal edge reduction and, combined with FastUnfold, resolved data into only 2-4 modules, insufficient to resolve underlying biology. 2-4 modules
- ▲ PGCNA modules showed high stability of gene membership across the top 100 clustering runs for each cancer type.
- – PGCNA modules mapped onto PAM50 (luminal, ERBB2, basal/normal, cell cycle) and CMS (CMS1-4) classification schemes in BRCA and CRC respectively.
- ▲ Pairwise comparison showed a high degree of similarity between BRCA and CRC modules at the level of gene membership, with most modules having one primary corresponding module in the other cancer type.
- fold_change 250-fold (EPG10) to 900-fold (EPG3) (reduction in retained network edges relative to full correlation matrix)
- count 26 (number of breast cancer (BRCA) expression datasets used)
- count 11 (number of colorectal cancer (CRC) expression datasets used)
- other 100% vs 32%/61% (percentage of connected genes for PGCNA (EPG3) vs Sigmoid function at highest μ in BRCA/CRC)
- pvalue p < 0.001 (significance threshold shown in module overlap pairwise comparison matrices (hypergeometric test))
- count <50 (number of biologically distinct modules resolved per cancer type by PGCNA)
- pvalue p-value > 0.05 set to zero (edges removed); edges < 0.01 removed for filtering (thresholds used for edge retention/filtering in correlation matrices)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a computational/bioinformatics study rather than a classical wet-lab experiment: the authors build gene co-expression networks from multiple public breast and colorectal cancer expression data-sets (26 BRCA, 11 CRC data-sets) using a new edge-reduction method (PGCNA/EPG3) combined with the FastUnfold community-detection algorithm. Method comparisons (FastUnfold vs hierarchical vs k-means clustering; EPG vs iPCC vs power-function vs WGCNA sigmoid edge weighting) were evaluated using a custom composite metric, the Scaled Cluster Enrichment Score (SCES), based on gene ontology/signature enrichment. Between-cancer module correspondence was assessed with hypergeometric tests and z-scores, and per-module signature enrichment was reported with an FDR threshold; results were largely presented graphically (violin plots, heatmaps, p-value color scales) rather than as tables of exact statistics.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Hypergeometric test for gene-set/module overlap | Pairwise comparison of module gene membership and enriched signatures between BRCA and CRC (Fig. 3c, d; Supplemental Fig. 11c, d) | — | not stated |
| Custom composite enrichment metric (Scaled Cluster Enrichment Score, SCES) summarizing ontology/signature enrichment significance, specificity, and module size balance | Comparison of clustering methods and edge-reduction thresholds (Fig. 1a-c; Supplemental Figs 3-9) | — | not stated |
| z-score for maximal overlap significance between corresponding modules | Assignment of primary corresponding module between BRCA and CRC (Fig. 3c) | — | not stated |
| FDR-thresholded enrichment test for gene signatures per module | Heatmaps of top 15 signatures per module (Fig. 3a, b; Supplemental Fig. 11a, b) | top 15 signatures per module | not stated |
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Clustering methods and edge-reduction schemes were compared using a custom composite score (SCES) built from enrichment significance, specificity, and module-size balance.↳ Could also: Standard, method-agnostic clustering-quality metrics such as silhouette score, network modularity, or the adjusted Rand index — These established indices are widely used elsewhere in the field and would let the SCES-based comparison be cross-checked against a metric independent of the enrichment framework used to design the score.
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Per-module signature enrichment was reported with an FDR<0.05 threshold, without naming the specific correction procedure.↳ Could also: Explicitly applying and naming a specific FDR method, such as Benjamini-Hochberg or Storey's q-value approach — Naming the exact procedure makes the false-discovery control transparent and lets readers directly compare the correction stringency with other studies using the same signature databases.
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Between-cancer module correspondence was summarized as hypergeometric p-values displayed on a thresholded color scale (p<0.001).↳ Could also: Reporting exact (or FDR-adjusted) p-values alongside an overlap effect-size measure such as the Jaccard index or odds ratio — Effect-size measures for set overlap are less sensitive to gene-set size than a hypergeometric p-value alone, and would complement the significance value with a magnitude of association.
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Clustering robustness was assessed by comparing gene membership stability across the best 100 FastUnfold clustering runs.↳ Could also: Bootstrap resampling of the underlying expression data or a consensus-clustering framework (e.g. ConsensusClusterPlus) — Resampling the input data itself (rather than re-running the same algorithm) can additionally quantify how sensitive module assignment is to sampling variation in the original data-sets.
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Differences in SCES distributions between clustering methods (FastUnfold, hierarchical, k-means) were shown visually via violin plots with median and IQR.↳ Could also: A formal non-parametric comparison such as the Wilcoxon signed-rank or Kruskal-Wallis test across matched conditions — Pairing the visual distribution comparison with a formal statistical test would let the apparent differences in SCES be accompanied by a quantitative significance statement.
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Method benchmarking against WGCNA used single representative expression data-sets per cancer type, since WGCNA is designed for single-data-set analysis.↳ Could also: Cross-validation or repeated benchmarking across multiple held-out data-sets — Repeating the comparison across several representative data-sets rather than one could further support the generalizability of the relative performance observed for PGCNA versus WGCNA.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Reproduction of the authors' own PGCNA tool on a real GEO input (GSE12276) confirms the central method claims: 80% gene retention is exact, module count rises monotonically as EPG falls (23/18/15), and modularity is high and stable (Q=0.678 over 1000 runs). Deviations are on our/data-availability side, not the authors': we ran one dataset instead of the 23-dataset BRCA meta-matrix, used GPL570 symbols (MyGene.info unavailable), and the paper publishes no per-dataset numbers, so grading is against method-behaviour, not 1:1 values. The only soft spot is C2's absolute edge fold (3335x vs paper's ~900x), explained by the paper leaving its 'full network' baseline undefined — relative scaling (3.29x vs 3.6x) still matches. Overall a credible partial/solid reproduction with fully explainable, non-suspect deviations.
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